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Add multi-class, multi-label and regression to transformers (#11012)

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* add to  bert

* review comments

* Update src/transformers/configuration_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/configuration_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* self.config.problem_type

* fix style

* fix

* fin

* fix

* update doc

* fix

* test

* Test more problem types

* Update src/transformers/configuration_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix

* remove

* fix

* quality

* make fix-copies

* remove test

Co-authored-by: abhishek thakur <abhishekkrthakur@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
This commit is contained in:
abhishek thakur
2021-05-04 08:23:40 +02:00
committed by GitHub
parent 7c622482e8
commit c40c7e213b
30 changed files with 298 additions and 76 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
tests/test_modeling_albert.py
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@@ -230,6 +230,8 @@ class AlbertModelTest(ModelTesterMixin, unittest.TestCase):
else ()
)
test_sequence_classification_problem_types = True
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

1
tests/test_modeling_bert.py
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@@ -439,6 +439,7 @@ class BertModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
else ()
)
all_generative_model_classes = (BertLMHeadModel,) if is_torch_available() else ()
test_sequence_classification_problem_types = True
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

1
tests/test_modeling_big_bird.py
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@@ -433,6 +433,7 @@ class BigBirdModelTest(ModelTesterMixin, unittest.TestCase):
# head masking & pruning is currently not supported for big bird
test_head_masking = False
test_pruning = False
test_sequence_classification_problem_types = True
# torchscript should be possible, but takes prohibitively long to test.
# Also torchscript is not an important feature to have in the beginning.

37
tests/test_modeling_common.py
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@@ -89,6 +89,7 @@ class ModelTesterMixin:
test_missing_keys = True
test_model_parallel = False
is_encoder_decoder = False
test_sequence_classification_problem_types = False
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
@@ -1238,6 +1239,42 @@ class ModelTesterMixin:
model.parallelize()
model.generate(**cast_to_device(inputs_dict, "cuda:0"), num_beams=2)
def test_problem_types(self):
if not self.test_sequence_classification_problem_types:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
problem_types = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if model_class not in get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
continue
for problem_type in problem_types:
with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"):
config.problem_type = problem_type["title"]
config.num_labels = problem_type["num_labels"]
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
if problem_type["num_labels"] > 1:
inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"])
inputs["labels"] = inputs["labels"].to(problem_type["dtype"])
loss = model(**inputs).loss
loss.backward()
global_rng = random.Random()

1
tests/test_modeling_convbert.py
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@@ -260,6 +260,7 @@ class ConvBertModelTest(ModelTesterMixin, unittest.TestCase):
)
test_pruning = False
test_head_masking = False
test_sequence_classification_problem_types = True
def setUp(self):
self.model_tester = ConvBertModelTester(self)

1
tests/test_modeling_distilbert.py
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@@ -211,6 +211,7 @@ class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
test_pruning = True
test_torchscript = True
test_resize_embeddings = True
test_sequence_classification_problem_types = True
def setUp(self):
self.model_tester = DistilBertModelTester(self)

1
tests/test_modeling_electra.py
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@@ -287,6 +287,7 @@ class ElectraModelTest(ModelTesterMixin, unittest.TestCase):
if is_torch_available()
else ()
)
test_sequence_classification_problem_types = True
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

1
tests/test_modeling_funnel.py
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@@ -360,6 +360,7 @@ class FunnelModelTest(ModelTesterMixin, unittest.TestCase):
if is_torch_available()
else ()
)
test_sequence_classification_problem_types = True
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

1
tests/test_modeling_longformer.py
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@@ -274,6 +274,7 @@ class LongformerModelTester:
class LongformerModelTest(ModelTesterMixin, unittest.TestCase):
test_pruning = False # pruning is not supported
test_torchscript = False
test_sequence_classification_problem_types = True
all_model_classes = (
(

1
tests/test_modeling_mobilebert.py
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@@ -267,6 +267,7 @@ class MobileBertModelTest(ModelTesterMixin, unittest.TestCase):
if is_torch_available()
else ()
)
test_sequence_classification_problem_types = True
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

1
tests/test_modeling_reformer.py
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@@ -590,6 +590,7 @@ class ReformerLocalAttnModelTest(ReformerTesterMixin, GenerationTesterMixin, Mod
test_pruning = False
test_headmasking = False
test_torchscript = False
test_sequence_classification_problem_types = True
def prepare_kwargs(self):
return {

1
tests/test_modeling_roberta.py
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@@ -351,6 +351,7 @@ class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCas
else ()
)
all_generative_model_classes = (RobertaForCausalLM,) if is_torch_available() else ()
test_sequence_classification_problem_types = True
def setUp(self):
self.model_tester = RobertaModelTester(self)

1
tests/test_modeling_squeezebert.py
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@@ -231,6 +231,7 @@ class SqueezeBertModelTest(ModelTesterMixin, unittest.TestCase):
test_torchscript = True
test_resize_embeddings = True
test_head_masking = False
test_sequence_classification_problem_types = True
def setUp(self):
self.model_tester = SqueezeBertModelTester(self)

1
tests/test_modeling_xlm.py
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@@ -349,6 +349,7 @@ class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_generative_model_classes = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
test_sequence_classification_problem_types = True
# XLM has 2 QA models -> need to manually set the correct labels for one of them here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):

1
tests/test_modeling_xlnet.py
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@@ -526,6 +526,7 @@ class XLNetModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase)
(XLNetLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
test_pruning = False
test_sequence_classification_problem_types = True
# XLNet has 2 QA models -> need to manually set the correct labels for one of them here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):